ATAD5 functions as a regulatory platform for Ub-PCNA deubiquitination.

Ubiquitination status of proliferating cell nuclear antigen (PCNA) is crucial for regulating DNA lesion bypass. After the resolution of fork stalling, PCNA is subsequently deubiquitinated, but the underlying mechanism remains undefined. We found that the N-terminal domain of ATAD5 (ATAD5-N), the largest subunit of the PCNA-unloading complex, functions as a scaffold for Ub-PCNA deubiquitination. ATAD5 recognizes DNA-loaded Ub-PCNA through distinct DNA-binding and PCNA-binding motifs. Furthermore, ATAD5 forms a heterotrimeric complex with UAF1-USP1 deubiquitinase, facilitating the deubiquitination of DNA-loaded Ub-PCNA. ATAD5 also enhances the Ub-PCNA deubiquitination by USP7 and USP11 through specific interactions. ATAD5 promotes the distinct deubiquitination process of UAF1-USP1, USP7, and USP11 for poly-Ub-PCNA. Additionally, ATAD5 mutants deficient in UAF1-binding had increased sensitivity to DNA-damaging agents. Our results ultimately reveal that ATAD5 and USPs cooperate to efficiently deubiquitinate Ub-PCNA prior to its release from the DNA in order to safely deactivate the DNA repair process.

Attention-based scale sequence network for small object detection.

Recently, with the remarkable development of deep learning technology, achievements are being updated in various computer vision fields. In particular, the object recognition field is receiving the most attention. Nevertheless, recognition performance for small objects is still challenging. Its performance is of utmost importance in realistic applications such as searching for missing persons through aerial photography. The core structure of the object recognition neural network is the feature pyramid network (FPN). You Only Look Once (YOLO) is the most widely used representative model following this structure. In this study, we proposed an attention-based scale sequence network (ASSN) that improves the scale sequence feature pyramid network (ssFPN), enhancing the performance of the FPN-based detector for small objects. ASSN is a lightweight attention module optimized for FPN-based detectors and has the versatility to be applied to any model with a corresponding structure. The proposed ASSN demonstrated performance improvements compared to the baselines (YOLOv7 and YOLOv8) in average precision (AP) of up to 0.6%. Additionally, the AP for small objects ( A P S ) showed also improvements of up to 1.9%. Furthermore, ASSN exhibits higher performance than ssFPN while achieving lightweightness and optimization, thereby improving computational complexity and processing speed. ASSN is open-source based on YOLO version 7 and 8. This can be found in our public repository: https://github.com/smu-ivpl/ASSN.git.

HDAC6 Inhibition Releases HR23B to Activate Proteasomes, Expand the Tumor Immunopeptidome and Amplify T-cell Antimyeloma Activity.

Proteasomes degrade intracellular proteins to generate antigenic peptides that are recognized by the adaptive immune system and promote anticancer immunity. However, tumors subvert the antigen presentation machinery to escape immunosurveillance. We hypothesized that proteasome activation could concomitantly increase antigen abundance and diversity in multiple myeloma cells. High-throughput screens revealed that histone deacetylase 6 (HDAC6) inhibitors activated proteasomes to unmask neoantigens and amplify the tumor-specific antigenic landscape. Treatment of patient CD138+ cells with HDAC6 inhibitors significantly promoted the antimyeloma activity of autologous CD8+ T cells. Pharmacologic blockade and genetic ablation of the HDAC6 ubiquitin-binding domain released HR23B, which shuttles ubiquitinylated cargo to proteasomes, while silencing HDAC6 or HR23B in multiple myeloma cells abolished the effect of HDAC6 inhibitors on proteasomes, antigen presentation, and T-cell cytotoxicity. Taken together, our results demonstrate the paradigm-shifting translational impact of proteasome activators to expand the myeloma immunopeptidome and have revealed novel, actionable antigenic targets for T cell-directed immunotherapy. SIGNIFICANCE: The elimination of therapy-resistant tumor cells remains a major challenge in the treatment of multiple myeloma. Our study identifies and functionally validates agents that amplify MHC class I-presented antigens and pave the way for the development of proteasome activators as immune adjuvants to enhance immunotherapeutic responses in patients with multiple myeloma.

Piezo1 facilitates optimal T cell activation during tumor challenge.

Functional effector T cells in the tumor microenvironment (TME) are critical for successful anti-tumor responses. T cell anti-tumor function is dependent on their ability to differentiate from a naïve state, infiltrate into the tumor site, and exert cytotoxic functions. The factors dictating whether a particular T cell can successfully undergo these processes during tumor challenge are not yet completely understood. Piezo1 is a mechanosensitive cation channel with high expression on both CD4+ and CD8+ T cells. Previous studies have demonstrated that Piezo1 optimizes T cell activation and restrains the CD4+ regulatory T cell (Treg) pool in vitro and under inflammatory conditions in vivo. However, little is known about the role Piezo1 plays on CD4+ and CD8+ T cells in cancer. We hypothesized that disruption of Piezo1 on T cells impairs anti-tumor immunity in vivo by hindering inflammatory T cell responses. We challenged mice with T cell Piezo1 deletion (P1KO) with tumor models dependent on T cells for immune rejection. P1KO mice had the more aggressive tumors, higher tumor growth rates and were unresponsive to immune-mediated therapeutic interventions. We observed a decreased CD4:CD8 ratio in both the secondary lymphoid organs and TME of P1KO mice that correlated inversely with tumor size. Poor CD4+ helper T cell responses underpinned the immunodeficient phenotype of P1KO mice. Wild type CD8+ T cells are sub-optimally activated in vivo with P1KO CD4+ T cells, taking on a CD25loPD-1hi phenotype. Together, our results suggest that Piezo1 optimizes T cell activation in the context of a tumor response.

Explainable AI for Medical Image Analysis in Medical Cyber-Physical Systems: Enhancing Transparency and Trustworthiness of IoMT.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

Proposal of a Token-Based Node Selection Mechanism for Node Distribution of Mobility IoT Blockchain Nodes.

Various elements, such as evolutions in IoT services resulting from sensoring by vehicle parts and advances in small communication technology devices, have significantly impacted the mass spread of mobility services that are provided to users in need of limited resources. In particular, business models are progressing away from one-off costs towards longer-term costs, as represented by shared services utilizing kick-boards or bicycles and subscription services for vehicle software. Advances in shared mobility services, as described, are calling for solutions that can enhance the reliability of data aggregated by users leveraging mobility services in the next-generation mobility areas. However, the mining process to renew status ensures continued network communication, and block creation demands high performance in the public block chain. Therefore, easing the mining process for state updates in public blockchains is a way to alleviate the high-performance process requirements of public blockchains. The proposed mechanism assigns token-based block creation authority instead of the mining method, which provides block creation authority to nodes that provide many resources. Blocks are created only by a group of participants with tokens, and after creation, tokens are updated and delivered to new nodes to form a new token group. Additionally, tokens are updated in each block after their initial creation, making it difficult to disguise the tokens and preventing resource-centered centralization.

Peptidylarginine deiminase 2 plays a key role in osteogenesis by enhancing RUNX2 stability through citrullination.

Peptidylarginine deiminase (PADI) 2 catalyzes the post-translational conversion of peptidyl-arginine to peptidyl-citrulline in a process called citrullination. However, the precise functions of PADI2 in bone formation and homeostasis remain unknown. In this study, our objective was to elucidate the function and regulatory mechanisms of PADI2 in bone formation employing global and osteoblast-specific Padi2 knockout mice. Our findings demonstrate that Padi2 deficiency leads to the loss of bone mass and results in a cleidocranial dysplasia (CCD) phenotype with delayed calvarial ossification and clavicular hypoplasia, due to impaired osteoblast differentiation. Mechanistically, Padi2 depletion significantly reduces RUNX2 levels, as PADI2-dependent stabilization of RUNX2 protected it from ubiquitin-proteasomal degradation. Furthermore, we discovered that PADI2 binds to RUNX2 and citrullinates it, and identified ten PADI2-induced citrullination sites on RUNX2 through high-resolution LC-MS/MS analysis. Among these ten citrullination sites, the R381 mutation in mouse RUNX2 isoform 1 considerably reduces RUNX2 levels, underscoring the critical role of citrullination at this residue in maintaining RUNX2 protein stability. In conclusion, these results indicate that PADI2 plays a distinct role in bone formation and osteoblast differentiation by safeguarding RUNX2 against proteasomal degradation. In addition, we demonstrate that the loss-of-function of PADI2 is associated with CCD, thereby providing a new target for the treatment of bone diseases.

ssFPN: Scale Sequence (S2) Feature-Based Feature Pyramid Network for Object Detection.

Object detection is a fundamental task in computer vision. Over the past several years, convolutional neural network (CNN)-based object detection models have significantly improved detection accuracyin terms of average precision (AP). Furthermore, feature pyramid networks (FPNs) are essential modules for object detection models to consider various object scales. However, the AP for small objects is lower than the AP for medium and large objects. It is difficult to recognize small objects because they do not have sufficient information, and information is lost in deeper CNN layers. This paper proposes a new FPN model named ssFPN (scale sequence (S2) feature-based feature pyramid network) to detect multi-scale objects, especially small objects. We propose a new scale sequence (S2) feature that is extracted by 3D convolution on the level of the FPN. It is defined and extracted from the FPN to strengthen the information on small objects based on scale-space theory. Motivated by this theory, the FPN is regarded as a scale space and extracts a scale sequence (S2) feature by three-dimensional convolution on the level axis of the FPN. The defined feature is basically scale-invariant and is built on a high-resolution pyramid feature map for small objects. Additionally, the deigned S2 feature can be extended to most object detection models based on FPNs. We also designed a feature-level super-resolution approach to show the efficiency of the scale sequence (S2) feature. We verified that the scale sequence (S2) feature could improve the classification accuracy for low-resolution images by training a feature-level super-resolution model. To demonstrate the effect of the scale sequence (S2) feature, experiments on the scale sequence (S2) feature built-in object detection approach including both one-stage and two-stage models were conducted on the MS COCO dataset. For the two-stage object detection models Faster R-CNN and Mask R-CNN with the S2 feature, AP improvements of up to 1.6% and 1.4%, respectively, were achieved. Additionally, the APS of each model was improved by 1.2% and 1.1%, respectively. Furthermore, the one-stage object detection models in the YOLO series were improved. For YOLOv4-P5, YOLOv4-P6, YOLOR-P6, YOLOR-W6, and YOLOR-D6 with the S2 feature, 0.9%, 0.5%, 0.5%, 0.1%, and 0.1% AP improvements were observed. For small object detection, the APS increased by 1.1%, 1.1%, 0.9%, 0.4%, and 0.1%, respectively. Experiments using the feature-level super-resolution approach with the proposed scale sequence (S2) feature were conducted on the CIFAR-100 dataset. By training the feature-level super-resolution model, we verified that ResNet-101 with the S2 feature trained on LR images achieved a 55.2% classification accuracy, which was 1.6% higher than for ResNet-101 trained on HR images.

The synthetic oleanane triterpenoid CDDO-2P-Im binds GRP78/BiP to induce unfolded protein response-mediated apoptosis in myeloma.

Synthetic oleanane triterpenoids (SOTs) are small molecules with broad anticancer properties. A recently developed SOT, 1-[2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oyl]-4(-pyridin-2-yl)-1H-imidazole (CDDO-2P-Im or '2P-Im'), exhibits enhanced activity and improved pharmacokinetics over CDDO-Im, a previous generation SOT. However, the mechanisms leading to these properties are not defined. Here, we show the synergy of 2P-Im and the proteasome inhibitor ixazomib in human multiple myeloma (MM) cells and 2P-Im activity in a murine model of plasmacytoma. RNA sequencing and quantitative reverse transcription PCR revealed the upregulation of the unfolded protein response (UPR) in MM cells upon 2P-lm treatment, implicating the activation of the UPR as a key step in 2P-Im-induced apoptosis. Supporting this hypothesis, the deletion of genes encoding either protein kinase R-like endoplasmic reticulum kinase (PERK) or DNA damage-inducible transcript 3 protein (DDIT3; also known as CHOP) impaired the MM response to 2P-Im, as did treatment with ISRIB, integrated stress response inhibitor, which inhibits UPR signaling downstream of PERK. Finally, both drug affinity responsive target stability and thermal shift assays demonstrated direct binding of 2P-Im to endoplasmic reticulum chaperone BiP (GRP78/BiP), a stress-inducible key signaling molecule of the UPR. These data reveal GRP78/BiP as a novel target of SOTs, and specifically of 2P-Im, and suggest the potential broader utility of this class of small molecules as modulators of the UPR.

Oral TGF-ßR1 inhibitor Vactosertib promotes osteosarcoma regression by targeting tumor proliferation and enhancing anti-tumor immunity.

Osteosarcoma (OS) is an aggressive malignant bone cancer, with refractory and metastatic disease remaining a significant challenge. Transforming growth factor-ß1 (TGF-ß) is a potent immune suppressive cytokine in OS and the TGF-ß is increased in the sera of OS patients and this increase is associated with high-grade OS and lung metastases. Therefore, blocking TGF-ß1 signaling may be a novel therapy for OS treatment. Here we show that blocking TGF-ß1 signaling using TGF-ßR1 inhibitor, Vactosertib, significantly inhibited OS proliferation in vitro and in vivo. Notably, Vactosertib inhibits c-Myc expression in the OS cells. Vactosertib increased immune effectors (IFNγ+CD8+ cells and NK cells) and inhibited immune suppressors (M2-like TAM, MDSC) in the OS tumor microenvironment. Our results suggest that inhibition of TGF-ß1 signaling is an effective therapeutic strategy against OS through a multi-pronged approach that targets tumor intrinsic and extrinsic factors to achieve optimal immune-effector functions and maximal clinical response.

Isolation and identification of extracellular matrix proteins from oil-based CASPERized mouse brains for matrisomal analysis.

The extracellular matrix (ECM) components present within all tissues and organs help to maintain the cytoskeletal architecture and tissue morphology. Although the ECM plays a role in cellular events and signaling pathways, it has not been well studied due its insolubility and complexity. Brain tissue has a higher cell density and weaker mechanical strength than other tissues in the body. When removing cells using a general decellularization method to produce scaffolds and obtain ECM proteins, various problems must be considered because tissues are easily damaged. To retain the brain shape and ECM components, we performed decellularization in combination with polymerization. We immersed mouse brains in oil for polymerization and decellularization via O-CASPER (Oil-based Clinically and Experimentally Applicable Acellular Tissue Scaffold Production for Tissue Engineering and Regenerative Medicine) and then isolated ECM components using sequential matrisome preparation reagents (SMPRs), namely, RIPA, PNGase F, and concanavalin A. Adult mouse brains were preserved with our decellularization method. Western blot and LC-MS/MS analyses revealed that ECM components, including collagen and laminin, were isolated efficiently from decellularized mouse brains using SMPRs. Our method will be useful to obtain matrisomal data and perform functional studies using adult mouse brains and other tissues.

A disease-associated XPA allele interferes with TFIIH binding and primarily affects transcription-coupled nucleotide excision repair.

XPA is a central scaffold protein that coordinates the assembly of repair complexes in the global genome (GG-NER) and transcription-coupled nucleotide excision repair (TC-NER) subpathways. Inactivating mutations in XPA cause xeroderma pigmentosum (XP), which is characterized by extreme UV sensitivity and a highly elevated skin cancer risk. Here, we describe two Dutch siblings in their late forties carrying a homozygous H244R substitution in the C-terminus of XPA. They present with mild cutaneous manifestations of XP without skin cancer but suffer from marked neurological features, including cerebellar ataxia. We show that the mutant XPA protein has a severely weakened interaction with the transcription factor IIH (TFIIH) complex leading to an impaired association of the mutant XPA and the downstream endonuclease ERCC1-XPF with NER complexes. Despite these defects, the patient-derived fibroblasts and reconstituted knockout cells carrying the XPA-H244R substitution show intermediate UV sensitivity and considerable levels of residual GG-NER (~50%), in line with the intrinsic properties and activities of the purified protein. By contrast, XPA-H244R cells are exquisitely sensitive to transcription-blocking DNA damage, show no detectable recovery of transcription after UV irradiation, and display a severe deficiency in TC-NER-associated unscheduled DNA synthesis. Our characterization of a new case of XPA deficiency that interferes with TFIIH binding and primarily affects the transcription-coupled subpathway of nucleotide excision repair, provides an explanation of the dominant neurological features in these patients, and reveals a specific role for the C-terminus of XPA in TC-NER.

Attention-Based Bi-Prediction Network for Versatile Video Coding (VVC) over 5G Network.

As the demands of various network-dependent services such as Internet of things (IoT) applications, autonomous driving, and augmented and virtual reality (AR/VR) increase, the fifthgeneration (5G) network is expected to become a key communication technology. The latest video coding standard, versatile video coding (VVC), can contribute to providing high-quality services by achieving superior compression performance. In video coding, inter bi-prediction serves to improve the coding efficiency significantly by producing a precise fused prediction block. Although block-wise methods, such as bi-prediction with CU-level weight (BCW), are applied in VVC, it is still difficult for the linear fusion-based strategy to represent diverse pixel variations inside a block. In addition, a pixel-wise method called bi-directional optical flow (BDOF) has been proposed to refine bi-prediction block. However, the non-linear optical flow equation in BDOF mode is applied under assumptions, so this method is still unable to accurately compensate various kinds of bi-prediction blocks. In this paper, we propose an attention-based bi-prediction network (ABPN) to substitute for the whole existing bi-prediction methods. The proposed ABPN is designed to learn efficient representations of the fused features by utilizing an attention mechanism. Furthermore, the knowledge distillation (KD)- based approach is employed to compress the size of the proposed network while keeping comparable output as the large model. The proposed ABPN is integrated into the VTM-11.0 NNVC-1.0 standard reference software. When compared with VTM anchor, it is verified that the BD-rate reduction of the lightweighted ABPN can be up to 5.89% and 4.91% on Y component under random access (RA) and low delay B (LDB), respectively.

Enhanced Ca2+-channeling complex formation at the ER-mitochondria interface underlies the pathogenesis of alcohol-associated liver disease.

Ca2+ overload-induced mitochondrial dysfunction is considered as a major contributing factor in the pathogenesis of alcohol-associated liver disease (ALD). However, the initiating factors that drive mitochondrial Ca2+ accumulation in ALD remain elusive. Here, we demonstrate that an aberrant increase in hepatic GRP75-mediated mitochondria-associated ER membrane (MAM) Ca2+-channeling (MCC) complex formation promotes mitochondrial dysfunction in vitro and in male mouse model of ALD. Unbiased transcriptomic analysis reveals PDK4 as a prominently inducible MAM kinase in ALD. Analysis of human ALD cohorts further corroborate these findings. Additional mass spectrometry analysis unveils GRP75 as a downstream phosphorylation target of PDK4. Conversely, non-phosphorylatable GRP75 mutation or genetic ablation of PDK4 prevents alcohol-induced MCC complex formation and subsequent mitochondrial Ca2+ accumulation and dysfunction. Finally, ectopic induction of MAM formation reverses the protective effect of PDK4 deficiency in alcohol-induced liver injury. Together, our study defines a mediatory role of PDK4 in promoting mitochondrial dysfunction in ALD.

Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.

With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.

Regulation of BRCA1 stability through the tandem UBX domains of isoleucyl-tRNA synthetase 1.

Aminoacyl-tRNA synthetases (ARSs) have evolved to acquire various additional domains. These domains allow ARSs to communicate with other cellular proteins in order to promote non-translational functions. Vertebrate cytoplasmic isoleucyl-tRNA synthetases (IARS1s) have an uncharacterized unique domain, UNE-I. Here, we present the crystal structure of the chicken IARS1 UNE-I complexed with glutamyl-tRNA synthetase 1 (EARS1). UNE-I consists of tandem ubiquitin regulatory X (UBX) domains that interact with a distinct hairpin loop on EARS1 and protect its neighboring proteins in the multi-synthetase complex from degradation. Phosphomimetic mutation of the two serine residues in the hairpin loop releases IARS1 from the complex. IARS1 interacts with BRCA1 in the nucleus, regulates its stability by inhibiting ubiquitylation via the UBX domains, and controls DNA repair function.

Pyruvate dehydrogenase kinase 4 promotes ubiquitin-proteasome system-dependent muscle atrophy.

BACKGROUND: Muscle atrophy, leading to muscular dysfunction and weakness, is an adverse outcome of sustained period of glucocorticoids usage. However, the molecular mechanism underlying this detrimental condition is currently unclear. Pyruvate dehydrogenase kinase 4 (PDK4), a central regulator of cellular energy metabolism, is highly expressed in skeletal muscle and has been implicated in the pathogenesis of several diseases. The current study was designed to investigated and delineate the role of PDK4 in the context of muscle atrophy, which could be identified as a potential therapeutic avenue to protect against dexamethasone-induced muscle wasting. METHODS: The dexamethasone-induced muscle atrophy in C2C12 myotubes was evaluated at the molecular level by expression of key genes and proteins involved in myogenesis, using immunoblotting and qPCR analyses. Muscle dysfunction was studied in vivo in wild-type and PDK4 knockout mice treated with dexamethasone (25 mg/kg body weight, i.p., 10 days). Body weight, grip strength, muscle weight and muscle histology were assessed. The expression of myogenesis markers were analysed using qPCR, immunoblotting and immunoprecipitation. The study was extended to in vitro human skeletal muscle atrophy analysis. RESULTS: Knockdown of PDK4 was found to prevent glucocorticoid-induced muscle atrophy and dysfunction in C2C12 myotubes, which was indicated by induction of myogenin (0.3271 ± 0.102 vs 2.163 ± 0.192, ****P < 0.0001) and myosin heavy chain (0.3901 ± 0.047 vs. 0.7222 ± 0.082, **P < 0.01) protein levels and reduction of muscle atrophy F-box (10.77 ± 2.674 vs. 1.518 ± 0.172, **P < 0.01) expression. In dexamethasone-induced muscle atrophy model, mice with genetic ablation of PDK4 revealed increased muscle strength (162.1 ± 22.75 vs. 200.1 ± 37.09 g, ***P < 0.001) and muscle fibres (54.20 ± 11.85% vs. 84.07 ± 28.41%, ****P < 0.0001). To explore the mechanism, we performed coimmunoprecipitation and liquid chromatography-mass spectrometry analysis and found that myogenin is novel substrate of PDK4. PDK4 phosphorylates myogenin at S43/T57 amino acid residues, which facilitates the recruitment of muscle atrophy F-box to myogenin and leads to its subsequent ubiquitination and degradation. Finally, overexpression of non-phosphorylatable myogenin mutant using intramuscular injection prevented dexamethasone-induced muscle atrophy and preserved muscle fibres. CONCLUSIONS: We have demonstrated that PDK4 mediates dexamethasone-induced skeletal muscle atrophy. Mechanistically, PDK4 phosphorylates and degrades myogenin via recruitment of E3 ubiquitin ligase, muscle atrophy F-box. Rescue of muscle regeneration by genetic ablation of PDK4 or overexpression of non-phosphorylatable myogenin mutant indicates PDK4 as an amenable therapeutic target in muscle atrophy.

Smad4-deficient T cells promote colitis-associated colon cancer via an IFN-γ-dependent suppression of 15-hydroxyprostaglandin dehydrogenase.

Immune cells and the cytokines they produce are important mediators of the transition from colitis to colon cancer, but the mechanisms mediating this disease progression are poorly understood. Interferon gamma (IFN-γ) is known to contribute to the pathogenesis of colitis through immune modulatory mechanisms, and through direct effects on endothelial and epithelial homeostasis. Here we explore whether IFN-γ influences tumor progression by expanding the effector memory T cells (TEM) population and restricting the expression of tumor suppressors in a preclinical model of spontaneous colitis-associated colorectal cancer (CAC). We show that IFN-γ expression is significantly increased both in the T cells and the colonic mucosal epithelia of mice with a T cell-restricted deletion of the TGF-ß intermediate, SMAD4 (Smad4TKO). The increase of IFN-γ expression correlates with the onset of spontaneous CAC in Smad4TKO mice by 6 months of age. This phenotype is greatly ameliorated by the introduction of a germline deletion of IFN-γ in Smad4TKO mice (Smad4TKO/IFN-γKO, DKO). DKO mice had a significantly reduced incidence and progression of CAC, and a decrease in the number of mucosal CD4+ TEM cells, when compared to those of Smad4TKO mice. Similarly, the colon epithelia of DKO mice exhibited a non-oncogenic signature with a decrease in the expression of iNOS and p-STAT1, and a restoration of the tumor suppressor gene, 15-hydroxyprostaglandin dehydrogenase (15-PGDH). In vitro, treatment of human colon cancer cells with IFN-γ decreased the expression of 15-PGDH. Our data suggest that Smad4-deficient T cells promote CAC through mechanisms that include an IFN-γ-dependent suppression of the tumor suppressor 15-PGDH.

Noncanonical PDK4 action alters mitochondrial dynamics to affect the cellular respiratory status.

Dynamic regulation of mitochondrial morphology provides cells with the flexibility required to adapt and respond to electron transport chain (ETC) toxins and mitochondrial DNA-linked disease mutations, yet the mechanisms underpinning the regulation of mitochondrial dynamics machinery by these stimuli is poorly understood. Here, we show that pyruvate dehydrogenase kinase 4 (PDK4) is genetically required for cells to undergo rapid mitochondrial fragmentation when challenged with ETC toxins. Moreover, PDK4 overexpression was sufficient to promote mitochondrial fission even in the absence of mitochondrial stress. Importantly, we observed that the PDK4-mediated regulation of mitochondrial fission was independent of its canonical function, i.e., inhibitory phosphorylation of the pyruvate dehydrogenase complex (PDC). Phosphoproteomic screen for PDK4 substrates, followed by nonphosphorylatable and phosphomimetic mutations of the PDK4 site revealed cytoplasmic GTPase, Septin 2 (SEPT2), as the key effector molecule that acts as a receptor for DRP1 in the outer mitochondrial membrane to promote mitochondrial fission. Conversely, inhibition of the PDK4-SEPT2 axis could restore the balance in mitochondrial dynamics and reinvigorates cellular respiration in mitochondrial fusion factor, mitofusin 2-deficient cells. Furthermore, PDK4-mediated mitochondrial reshaping limits mitochondrial bioenergetics and supports cancer cell growth. Our results identify the PDK4-SEPT2-DRP1 axis as a regulator of mitochondrial function at the interface between cellular bioenergetics and mitochondrial dynamics.